"""
Copyright (c) 2005-2023, NumPy Developers.
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:

    * Redistributions of source code must retain the above copyright
       notice, this list of conditions and the following disclaimer.

    * Redistributions in binary form must reproduce the above
       copyright notice, this list of conditions and the following
       disclaimer in the documentation and/or other materials provided
       with the distribution.

    * Neither the name of the NumPy Developers nor the names of any
       contributors may be used to endorse or promote products derived
       from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""

from __future__ import annotations

import numpy as np


def _disables_array_ufunc(obj):
    """True when __array_ufunc__ is set to None."""
    try:
        return obj.__array_ufunc__ is None
    except AttributeError:
        return False


def _binary_method(ufunc, name):
    """Implement a forward binary method with a ufunc, e.g., __add__."""

    def func(self, other):
        if _disables_array_ufunc(other):
            return NotImplemented

        return ufunc(self, other)

    func.__name__ = f"__{name}__"
    return func


def _reflected_binary_method(ufunc, name):
    """Implement a reflected binary method with a ufunc, e.g., __radd__."""

    def func(self, other):
        if _disables_array_ufunc(other):
            return NotImplemented
        return ufunc(other, self)

    func.__name__ = f"__r{name}__"
    return func


def _inplace_binary_method(ufunc, name):
    """Implement an in-place binary method with a ufunc, e.g., __iadd__."""

    def func(self, other):
        return ufunc(self, other, out=(self,))

    func.__name__ = f"__i{name}__"
    return func


def _numeric_methods(ufunc, name):
    """Implement forward, reflected and inplace binary methods with a ufunc."""
    return (
        _binary_method(ufunc, name),
        _reflected_binary_method(ufunc, name),
        _inplace_binary_method(ufunc, name),
    )


def _unary_method(ufunc, name):
    """Implement a unary special method with a ufunc."""

    def func(self):
        return ufunc(self)

    func.__name__ = f"__{name}__"
    return func


class NDArrayOperatorsMixin:
    """Mixin defining all operator special methods using __array_ufunc__.

    This class implements the special methods for almost all of Python's
    builtin operators defined in the `operator` module, including comparisons
    (``==``, ``>``, etc.) and arithmetic (``+``, ``*``, ``-``, etc.), by
    deferring to the ``__array_ufunc__`` method, which subclasses must
    implement.

    It is useful for writing classes that do not inherit from `numpy.ndarray`,
    but that should support arithmetic and numpy universal functions like
    arrays as described in `A Mechanism for Overriding Ufuncs
    <https://numpy.org/neps/nep-0013-ufunc-overrides.html>`_.

    As an trivial example, consider this implementation of an ``ArrayLike``
    class that simply wraps a NumPy array and ensures that the result of any
    arithmetic operation is also an ``ArrayLike`` object::

        class ArrayLike(np.lib.mixins.NDArrayOperatorsMixin):
            def __init__(self, value):
                self.value = np.asarray(value)

            # One might also consider adding the built-in list type to this
            # list, to support operations like np.add(array_like, list)
            _HANDLED_TYPES = (np.ndarray, numbers.Number)

            def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
                out = kwargs.get('out', ())
                for x in inputs + out:
                    # Only support operations with instances of _HANDLED_TYPES.
                    # Use ArrayLike instead of type(self) for isinstance to
                    # allow subclasses that don't override __array_ufunc__ to
                    # handle ArrayLike objects.
                    if not isinstance(x, self._HANDLED_TYPES + (ArrayLike,)):
                        return NotImplemented

                # Defer to the implementation of the ufunc on unwrapped values.
                inputs = tuple(x.value if isinstance(x, ArrayLike) else x
                               for x in inputs)
                if out:
                    kwargs['out'] = tuple(
                        x.value if isinstance(x, ArrayLike) else x
                        for x in out)
                result = getattr(ufunc, method)(*inputs, **kwargs)

                if type(result) is tuple:
                    # multiple return values
                    return tuple(type(self)(x) for x in result)
                elif method == 'at':
                    # no return value
                    return None
                else:
                    # one return value
                    return type(self)(result)

            def __repr__(self):
                return '%s(%r)' % (type(self).__name__, self.value)

    In interactions between ``ArrayLike`` objects and numbers or numpy arrays,
    the result is always another ``ArrayLike``:

        >>> x = ArrayLike([1, 2, 3])
        >>> x - 1
        ArrayLike(array([0, 1, 2]))
        >>> 1 - x
        ArrayLike(array([ 0, -1, -2]))
        >>> np.arange(3) - x
        ArrayLike(array([-1, -1, -1]))
        >>> x - np.arange(3)
        ArrayLike(array([1, 1, 1]))

    Note that unlike ``numpy.ndarray``, ``ArrayLike`` does not allow operations
    with arbitrary, unrecognized types. This ensures that interactions with
    ArrayLike preserve a well-defined casting hierarchy.

    .. versionadded:: 1.13
    """

    # Like np.ndarray, this mixin class implements "Option 1" from the ufunc
    # overrides NEP.

    # comparisons don't have reflected and in-place versions
    __lt__ = _binary_method(np.less, "lt")
    __le__ = _binary_method(np.less_equal, "le")
    __eq__ = _binary_method(np.equal, "eq")
    __ne__ = _binary_method(np.not_equal, "ne")
    __gt__ = _binary_method(np.greater, "gt")
    __ge__ = _binary_method(np.greater_equal, "ge")

    # numeric methods
    __add__, __radd__, __iadd__ = _numeric_methods(np.add, "add")
    __sub__, __rsub__, __isub__ = _numeric_methods(np.subtract, "sub")
    __mul__, __rmul__, __imul__ = _numeric_methods(np.multiply, "mul")
    __matmul__, __rmatmul__, __imatmul__ = _numeric_methods(np.matmul, "matmul")
    # Python 3 does not use __div__, __rdiv__, or __idiv__
    __truediv__, __rtruediv__, __itruediv__ = _numeric_methods(
        np.true_divide, "truediv"
    )
    __floordiv__, __rfloordiv__, __ifloordiv__ = _numeric_methods(
        np.floor_divide, "floordiv"
    )
    __mod__, __rmod__, __imod__ = _numeric_methods(np.remainder, "mod")
    __divmod__ = _binary_method(np.divmod, "divmod")
    __rdivmod__ = _reflected_binary_method(np.divmod, "divmod")
    # __idivmod__ does not exist
    # TODO: handle the optional third argument for __pow__?
    __pow__, __rpow__, __ipow__ = _numeric_methods(np.power, "pow")
    __lshift__, __rlshift__, __ilshift__ = _numeric_methods(np.left_shift, "lshift")
    __rshift__, __rrshift__, __irshift__ = _numeric_methods(np.right_shift, "rshift")
    __and__, __rand__, __iand__ = _numeric_methods(np.bitwise_and, "and")
    __xor__, __rxor__, __ixor__ = _numeric_methods(np.bitwise_xor, "xor")
    __or__, __ror__, __ior__ = _numeric_methods(np.bitwise_or, "or")

    # unary methods
    __neg__ = _unary_method(np.negative, "neg")
    __pos__ = _unary_method(np.positive, "pos")
    __abs__ = _unary_method(np.absolute, "abs")
    __invert__ = _unary_method(np.invert, "invert")
